Dell BSAFE Crypto-C Micro Edition, version 4.1.5, and Dell BSAFE Micro Edition Suite, versions 4.0 through 4.6.1 and version 5.0, contains an Out-of-bounds Read vulnerability. An unauthenticated attacker with local access could potentially exploit this vulnerability, leading to Information exposure.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where an unprivileged regular user can cause an out-of-bounds read, which may lead to denial of service and information disclosure.

Out-of-bounds read in the parsing of image data in Samsung Notes prior to version 4.4.30.63 allows local attackers to access out-of-bounds memory.

Out-of-bounds read in the SPI decoder in Samsung Notes prior to version 4.4.30.63 allows local attackers to access out-of-bounds memory.

Out-of-bounds read in the reading of image data in Samsung Notes prior to version 4.4.30.63 allows local attackers to access out-of-bounds memory.

Out-of-bounds read in fingerprint trustlet prior to SMR May-2025 Release 1 allows local privileged attackers to read out-of-bounds memory.

Out-of-bounds read in parsing audio data in libsavsac.so prior to SMR Apr-2025 Release 1 allows local attackers to read out-of-bounds memory.

Out-of-bounds read in the allocation of image buffer in Samsung Notes prior to version 4.4.30.63 allows local attackers to access out-of-bounds memory.

A vulnerability was determined in WebAssembly Binaryen up to 125. Affected by this issue is the function WasmBinaryReader::readExport of the file src/wasm/wasm-binary.cpp. This manipulation causes heap-based buffer overflow. It is possible to launch the attack on the local host. The exploit has been publicly disclosed and may be utilized. Patch name: 4f52bff8c4075b5630422f902dd92a0af2c9f398. It is recommended to apply a patch to fix this issue.

Improper use of SMS buffer pointer in Shannon baseband prior to SMR Mar-2022 Release 1 allows OOB read.

Information disclosure due to buffer over read in kernel in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Mobile

A security out-of-bounds read information disclosure vulnerability in Trend Micro Worry-Free Business Security Server could allow a local attacker to send garbage data to a specific named pipe and crash the server. Please note: an attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.

Out-of-bounds Read in GitHub repository radareorg/radare2 prior to 5.7.0. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.

Buffer Over-read at parse_rawml.c:1416 in GitHub repository bfabiszewski/libmobi prior to 0.11. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.

Vulnerability of accessing invalid memory in the component driver module. Impact: Successful exploitation of this vulnerability will affect availability and confidentiality.

An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_send_action_frame(), there is no input validation check on len coming from userspace, which can lead to a heap over-read.

An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_send_action_frame_cert(), there is no input validation check on len coming from userspace, which can lead to a heap over-read.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `QuantizeAndDequantizeV*` operations can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

Vba32 Antivirus v3.36.0 is vulnerable to an Arbitrary Memory Read vulnerability. The 0x22200B IOCTL code of the Vba32m64.sys driver allows to read up to 0x802 of memory from ar arbitrary user-supplied pointer.

Vba32 Antivirus v3.36.0 is vulnerable to an Arbitrary Memory Read vulnerability by triggering the 0x22201B, 0x22201F, 0x222023, 0x222027 ,0x22202B, 0x22202F, 0x22203F, 0x222057 and 0x22205B IOCTL codes of the Vba32m64.sys driver.

Improper input validation in bootloader prior to SMR Feb-2024 Release 1 allows local privileged attackers to cause an Out-Of-Bounds read.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Possible buffer overflow due to lack of buffer length check during management frame Rx handling in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

An out-of-bounds array read in the apr_time_exp*() functions was fixed in the Apache Portable Runtime 1.6.3 release (CVE-2017-12613). The fix for this issue was not carried forward to the APR 1.7.x branch, and hence version 1.7.0 regressed compared to 1.6.3 and is vulnerable to the same issue.

Possible out of bound read due to lack of length check of data length for a DIAG event in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music

Redis is an open source, in-memory database that persists on disk. Versions 8.2.1 and below allow an authenticated user to use a specially crafted LUA script to read out-of-bound data or crash the server and subsequent denial of service. The problem exists in all versions of Redis with Lua scripting. This issue is fixed in version 8.2.2. To workaround this issue without patching the redis-server executable is to prevent users from executing Lua scripts. This can be done using ACL to block a script by restricting both the EVAL and FUNCTION command families.

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. The implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` argument. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Possible buffer over read due to improper buffer allocation for file length passed from user space in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

A local user may be able to cause unexpected system termination or read kernel memory. This issue is fixed in macOS Big Sur 11.4, Security Update 2021-003 Catalina. An out-of-bounds read issue was addressed by removing the vulnerable code.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Lack of boundary checking of a buffer in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows OOB read

Lack of boundary checking of a buffer in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows OOB read.

Out of bounds read in firmware for some Intel(R) Wireless Bluetooth(R) and Killer(TM) Bluetooth(R) products before version 22.120 may allow a privileged user to potentially enable information disclosure via local access.